# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np
from op_test import (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    is_custom_device,
)

import paddle
from paddle.base import core


def Heaviside_grad(x, y, dout, astype="float16", is_bfloat16=False):
    tmp = np.zeros(x.shape).astype(astype)
    dx = np.multiply(tmp, dout)
    dy = np.multiply(np.equal(x, 0), dout).astype(astype)
    if is_bfloat16:
        dx = convert_float_to_uint16(dx)
        dy = convert_float_to_uint16(dy)
    return dx, dy


class TestElementwiseOp(OpTest):
    def setUp(self):
        self.op_type = "elementwise_heaviside"
        x = np.random.random((13, 17)).astype("float64")
        y = np.random.random((13, 17)).astype("float64")
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {'X': x, 'Y': y}
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}

    def test_check_output(self):
        self.check_output(
            check_pir=True, check_prim_pir=True, check_symbol_infer=False
        )

    def test_check_grad_normal(self):
        self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)

    def test_check_grad_ignore_x(self):
        self.check_grad(
            ['Y'],
            'Out',
            no_grad_set=set("X"),
            check_pir=True,
            check_prim_pir=True,
        )

    def test_check_grad_ignore_y(self):
        self.check_grad(
            ['X'],
            'Out',
            no_grad_set=set('Y'),
            check_pir=True,
            check_prim_pir=True,
        )


class TestHeavisideBroadcast(unittest.TestCase):
    def setUp(self):
        self.input_1 = np.random.rand(2, 100, 13, 17).astype("float32")
        self.input_2 = np.random.rand(100, 13, 17).astype("float32")
        self.input_3 = np.random.rand(100, 13, 1).astype("float32")
        self.input_4 = np.random.rand(13, 17).astype("float32")
        self.input_5 = np.random.rand(1).astype("float32")

        self.np_expected1 = np.heaviside(self.input_1, self.input_2)
        self.np_expected2 = np.heaviside(self.input_2, self.input_3)
        self.np_expected3 = np.heaviside(self.input_2, self.input_4)
        self.np_expected4 = np.heaviside(self.input_4, self.input_5)

    def test_broadcast(self):
        paddle.disable_static()
        self.tensor_1 = paddle.to_tensor(self.input_1)
        self.tensor_2 = paddle.to_tensor(self.input_2)
        self.tensor_3 = paddle.to_tensor(self.input_3)
        self.tensor_4 = paddle.to_tensor(self.input_4)
        self.tensor_5 = paddle.to_tensor(self.input_5)

        res = paddle.heaviside(self.tensor_1, self.tensor_2)
        res = res.numpy()
        np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)

        res = paddle.heaviside(self.tensor_2, self.tensor_3)
        res = res.numpy()
        np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)

        res = paddle.heaviside(self.tensor_2, self.tensor_4)
        res = res.numpy()
        np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)

        res = paddle.heaviside(self.tensor_4, self.tensor_5)
        res = res.numpy()
        np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)


class TestHeavisideAPI_float64(unittest.TestCase):
    def setUp(self):
        self.x_np = np.random.random((13, 17)).astype("float64")
        self.y_np = np.random.random((13, 17)).astype("float64")
        self.out_np = np.heaviside(self.x_np, self.y_np)
        self.dtype = "float64"

    def test_static(self):
        for use_cuda in (
            [False, True]
            if (paddle.device.is_compiled_with_cuda() or is_custom_device())
            else [False]
        ):
            place = get_device_place() if use_cuda else paddle.CPUPlace()

            paddle.enable_static()
            prog = paddle.static.Program()
            with paddle.static.program_guard(prog):
                x = paddle.static.data(
                    name=f"x_{self.dtype}", shape=[13, 17], dtype=self.dtype
                )
                y = paddle.static.data(
                    name=f"y_{self.dtype}", shape=[13, 17], dtype=self.dtype
                )
                out = paddle.heaviside(x, y)

            exe = paddle.static.Executor(place=place)
            (res,) = exe.run(
                prog,
                feed={
                    f"x_{self.dtype}": self.x_np,
                    f"y_{self.dtype}": self.y_np,
                },
                fetch_list=out,
                use_prune=True,
            )

            np.testing.assert_allclose(res, self.out_np, rtol=1e-05)

    def test_dygraph(self):
        for use_cuda in (
            [False, True]
            if (paddle.device.is_compiled_with_cuda() or is_custom_device())
            else [False]
        ):
            place = get_device_place() if use_cuda else paddle.CPUPlace()
            paddle.disable_static(place=place)
            result = paddle.heaviside(
                paddle.to_tensor(self.x_np), paddle.to_tensor(self.y_np)
            )

            np.testing.assert_allclose(result.numpy(), self.out_np, rtol=1e-05)


class TestHeavisideAPI_float32(TestHeavisideAPI_float64):
    def setUp(self):
        self.x_np = np.random.random((13, 17)).astype("float32")
        self.y_np = np.random.random((13, 17)).astype("float32")
        self.out_np = np.heaviside(self.x_np, self.y_np)
        self.dtype = "float32"


class TestHeavisideAPI_int64(TestHeavisideAPI_float64):
    def setUp(self):
        self.x_np = np.random.random((13, 17)).astype("int64")
        self.y_np = np.random.random((13, 17)).astype("int64")
        self.out_np = np.heaviside(self.x_np, self.y_np)
        self.dtype = "int64"


class TestHeavisideAPI_int32(TestHeavisideAPI_float64):
    def setUp(self):
        self.x_np = np.random.random((13, 17)).astype("int32")
        self.y_np = np.random.random((13, 17)).astype("int32")
        self.out_np = np.heaviside(self.x_np, self.y_np)
        self.dtype = "int32"


class TestElementwiseOp1(TestElementwiseOp):
    def setUp(self):
        self.op_type = "elementwise_heaviside"
        x = np.random.random(100).astype("float64")
        y = np.random.random((13, 100)).astype("float64")
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {'X': x, 'Y': y}
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseOp2(TestElementwiseOp):
    def setUp(self):
        self.op_type = "elementwise_heaviside"
        x = np.random.random((13, 100)).astype("float64")
        y = np.random.random(100).astype("float64")
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {'X': x, 'Y': y}
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseOp3(TestElementwiseOp):
    def setUp(self):
        self.op_type = "elementwise_heaviside"
        x = np.random.uniform(-10, 10, [100]).astype("float64")
        y = np.random.uniform(-10, 10, [3, 100]).astype("float64")
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {'X': x, 'Y': y}
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseOp4(TestElementwiseOp):
    def setUp(self):
        self.op_type = "elementwise_heaviside"
        x = np.random.uniform(0, 10, []).astype("float64")
        y = np.random.uniform(-10, 0, [2, 3, 20]).astype("float64")
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {'X': x, 'Y': y}
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}


class TestHeavisideFP16Op(OpTest):
    def setUp(self):
        self.dtype = np.float16
        self.op_type = "elementwise_heaviside"
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {
            'X': np.random.uniform(1, 2, [20, 5]).astype("float16"),
            'Y': np.random.uniform(1, 2, [20, 5]).astype("float16"),
        }
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}

    def test_check_output(self):
        self.check_output(
            check_pir=True, check_prim_pir=True, check_symbol_infer=False
        )

    def test_check_grad(self):
        self.check_grad(
            ['X', 'Y'],
            'Out',
            user_defined_grads=Heaviside_grad(
                self.inputs['X'], self.inputs['Y'], 1 / self.inputs['X'].size
            ),
            check_pir=True,
            check_prim_pir=True,
        )


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestHeavisideBF16Op(OpTest):
    def setUp(self):
        self.dtype = np.uint16
        self.np_dtype = np.float32
        self.op_type = "elementwise_heaviside"
        self.python_api = paddle.heaviside
        self.prim_op_type = "comp"
        self.public_python_api = paddle.heaviside
        self.inputs = {
            'X': np.random.uniform(1, 2, [20, 5]).astype(self.np_dtype),
            'Y': np.random.uniform(1, 2, [20, 5]).astype(self.np_dtype),
        }
        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}

        self.place = get_device_place()
        self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
        self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
        self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])

    def test_check_output(self):
        self.check_output_with_place(
            self.place,
            check_pir=True,
            check_prim_pir=True,
            check_symbol_infer=False,
        )

    def test_check_grad(self):
        self.check_grad_with_place(
            self.place,
            ['X', 'Y'],
            'Out',
            user_defined_grads=Heaviside_grad(
                self.inputs['X'],
                self.inputs['Y'],
                1 / self.inputs['X'].size,
                self.np_dtype,
                True,
            ),
            check_pir=True,
            check_prim_pir=True,
        )


class TestHeavisideError(unittest.TestCase):
    def test_input(self):
        paddle.disable_static()

        def test_input_x():
            paddle.heaviside(1, paddle.randn([100]))

        self.assertRaises(ValueError, test_input_x)

        def test_input_y():
            paddle.heaviside(paddle.randn([100]), 1)

        self.assertRaises(ValueError, test_input_y)

        def test_input_xy():
            paddle.heaviside(
                paddle.randn([100], 'float32'), paddle.randn([100], 'float64')
            )

        self.assertRaises(ValueError, test_input_xy)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device()),
    "core is not compiled with CUDA",
)
class TestElementwiseHeavisideOp_Stride(OpTest):
    no_need_check_grad = True

    def setUp(self):
        self.op_type = "elementwise_heaviside"
        self.python_api = paddle.heaviside
        self.public_python_api = paddle.heaviside
        self.transpose_api = paddle.transpose
        self.as_stride_api = paddle.as_strided
        self.init_dtype()
        self.init_input_output()

        self.inputs_stride = {
            'X': OpTest.np_dtype_to_base_dtype(self.x),
            'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
        }

        self.inputs = {
            'X': OpTest.np_dtype_to_base_dtype(self.x),
            'Y': OpTest.np_dtype_to_base_dtype(self.y),
        }

        self.outputs = {'Out': self.out}

    def init_dtype(self):
        self.dtype = np.float64
        self.val_dtype = np.float64

    def test_check_output(self):
        place = get_device_place()
        self.check_strided_forward = True
        self.check_output(
            place,
        )

    def init_input_output(self):
        self.strided_input_type = "transpose"
        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.out = np.heaviside(self.x, self.y)
        self.perm = [1, 0]
        self.y_trans = np.transpose(self.y, self.perm)

    def test_check_gradient(self):
        pass


class TestElementwiseHeavisideOp_Stride1(TestElementwiseHeavisideOp_Stride):
    def init_input_output(self):
        self.strided_input_type = "transpose"
        self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
        self.out = np.heaviside(self.x, self.y)
        self.perm = [0, 1, 3, 2]
        self.y_trans = np.transpose(self.y, self.perm)


class TestElementwiseHeavisideOp_Stride2(TestElementwiseHeavisideOp_Stride):
    def init_input_output(self):
        self.strided_input_type = "transpose"
        self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
        self.out = np.heaviside(self.x, self.y)
        self.perm = [0, 2, 1, 3]
        self.y_trans = np.transpose(self.y, self.perm)


class TestElementwiseHeavisideOp_Stride3(TestElementwiseHeavisideOp_Stride):
    def init_input_output(self):
        self.strided_input_type = "transpose"
        self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
        self.out = np.heaviside(self.x, self.y)
        self.perm = [0, 1, 3, 2]
        self.y_trans = np.transpose(self.y, self.perm)


class TestElementwiseHeavisideOp_Stride4(TestElementwiseHeavisideOp_Stride):
    def init_input_output(self):
        self.strided_input_type = "transpose"
        self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
        self.out = np.heaviside(self.x, self.y)
        self.perm = [1, 0, 2, 3]
        self.y_trans = np.transpose(self.y, self.perm)


class TestElementwiseHeavisideOp_Stride5(TestElementwiseHeavisideOp_Stride):
    def init_input_output(self):
        self.strided_input_type = "as_stride"
        self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
        self.y_trans = self.y
        self.y = self.y[:, 0:1, :, 0:1]
        self.out = np.heaviside(self.x, self.y)
        self.shape_param = [23, 1, 13, 1]
        self.stride_param = [520, 260, 20, 1]


class TestElementwiseHeavisideOp_Stride_ZeroDim1(
    TestElementwiseHeavisideOp_Stride
):
    def init_input_output(self):
        self.strided_input_type = "transpose"
        self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.out = np.heaviside(self.x, self.y)
        self.perm = [1, 0]
        self.y_trans = np.transpose(self.y, self.perm)


class TestElementwiseHeavisideOp_Stride_ZeroSize1(
    TestElementwiseHeavisideOp_Stride
):
    def init_data(self):
        self.strided_input_type = "transpose"
        self.x = np.random.rand(1, 0, 2).astype('float32')
        self.y = np.random.rand(3, 0, 1).astype('float32')
        self.out = np.heaviside(self.x, self.y)
        self.perm = [2, 1, 0]
        self.y_trans = np.transpose(self.y, self.perm)


if __name__ == '__main__':
    unittest.main()
